| tags: | |
| - object-detection | |
| - fashion | |
| - conditional-detr | |
| license: apache-2.0 | |
| datasets: | |
| - baselefre/new_embeddings_fixed_cats | |
| # Fashion Object Detection Model | |
| Fine-tuned Conditional DETR model for detecting 8 fashion categories: | |
| - bag | |
| - bottom | |
| - dress | |
| - hat | |
| - outer | |
| - shoes | |
| - top | |
| - accessory | |
| ## Model Details | |
| - Base model: microsoft/conditional-detr-resnet-50 | |
| - Training dataset: baselefre/new_embeddings_fixed_cats | |
| - Checkpoint: 18000 steps | |
| ## Usage | |
| ```python | |
| from transformers import AutoImageProcessor, AutoModelForObjectDetection | |
| from PIL import Image | |
| import torch | |
| # Load model | |
| processor = AutoImageProcessor.from_pretrained("baselefre/objectdetectionaugmentedclean") | |
| model = AutoModelForObjectDetection.from_pretrained("baselefre/objectdetectionaugmentedclean") | |
| # Load image | |
| image = Image.open("your_image.jpg") | |
| # Inference | |
| inputs = processor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| target_sizes = torch.tensor([image.size[::-1]]) | |
| results = processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0] | |
| # Print detections | |
| for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): | |
| print(f"{model.config.id2label[label.item()]}: {score:.2f} at {box.tolist()}") | |
| ``` | |